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Databricks Certified Professional Data Engineer Exam Sample Questions (Q82-Q87):
NEW QUESTION # 82
Which of the following is true of Delta Lake and the Lakehouse?
- A. Z-order can only be applied to numeric values stored in Delta Lake tables
- B. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
- C. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
- D. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
- E. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
Answer: E
Explanation:
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
Parquet compresses data column by column, not row by row2. This allows for better compression ratios, especially for repeated or similar values within a column2.
Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3. Views are logical constructs that are defined by a SQL query on one or more base tables3. Views are not materialized by default, which means they do not store any data, but only the query definition3. Therefore, views always reflect the latest state of the source tables when queried3. However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.
NEW QUESTION # 83
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?
- A. Integration
- B. functional
- C. Unit
- D. Manual
Answer: C
Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
Reference:
Software Testing Fundamentals: Unit Testing
NEW QUESTION # 84
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources.
Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table.
Given the current implementation, which method can be used?
- A. Parse the Delta Lake transaction log to identify all newly written data files.
- B. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
- C. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
- D. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
Answer: C
Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.
References:
* Delta Lake Documentation on Time Travel: Delta Time Travel
* Delta Lake Versioning: Delta Lake Versioning Guide
NEW QUESTION # 85
Which of the following SQL statements can be used to update a transactions table, to set a flag on the table from Y to N
- A. REPLACE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
- B. MODIFY transactions SET active_flag = 'N' WHERE active_flag = 'Y'
- C. UPDATE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
- D. MERGE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
Answer: A
Explanation:
Explanation
The answer is
UPDATE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
Delta Lake supports UPDATE statements on the delta table, all of the changes as part of the update are ACID compliant.
NEW QUESTION # 86
When using the complete mode to write stream data, how does it impact the target table?
- A. Delta commits transaction once the stream is stopped
- B. Target table is overwritten for each batch
- C. Stream must complete to write the data
- D. Entire stream waits for complete data to write
- E. Target table cannot be updated while stream is pending
Answer: B
Explanation:
Explanation
The answer is Target table is overwritten for each batch
Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries
NEW QUESTION # 87
......
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